Detection of Wheat Crop Quality using Deep Convolution Neural Network
- Resource Type
- Conference
- Authors
- Sharma, Ranjana; Suyal, Priyanka; Dutt, Sarthika; Bharadwaj, Shambhu
- Source
- 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) System Modeling & Advancement in Research Trends (SMART), 2022 11th International Conference on. :1266-1269 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
General Topics for Engineers
Geoscience
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Computer vision
Image recognition
Convolution
Computational modeling
Crops
Market research
Feature extraction
Deep Learning
Feature Extraction
Convolutional Neural Network (CNN)
VGG16 Computer Vision
- Language
- ISSN
- 2767-7362
To recognition of disease by automatic is the most exciting and difficult issues in computer imaginative and prescient. A novel technique for disease identification is proposed in this paper. The proposed work guides a distinctive taking out solution for distinguishing between hale and hearty and dangerous crop “wheat” plants. To train the neural network “Convolutional Neural Network” (CNN) because of its capability applications, and CNNs have quickly become the go to tool for tackling any image data problem. The identification of disease in crop is one and most exciting or difficult issues in research imaginative and prescient. In this research paper we introduced a novel technique for identifying diseases. The proposed technique proposes the solution for feature extraction for distinguishing between hale and hearty and damaging wheat plants. To teach the model, we use convolutional neural network (CNN) for image categorization.